76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
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import numpy as np
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import torch
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from torch import nn
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from torch.functional import norm
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from torch.nn import init
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def XNorm(x, gamma):
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norm_tensor = torch.norm(x, 2, -1, True)
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return x * gamma / norm_tensor
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class UFOAttention(nn.Module):
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'''
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Scaled dot-product attention
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'''
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def __init__(self, d_model, d_k, d_v, h, dropout=.1):
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'''
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:param d_model: Output dimensionality of the model
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:param d_k: Dimensionality of queries and keys
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:param d_v: Dimensionality of values
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:param h: Number of heads
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'''
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super(UFOAttention, self).__init__()
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self.fc_q = nn.Linear(d_model, h * d_k)
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self.fc_k = nn.Linear(d_model, h * d_k)
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self.fc_v = nn.Linear(d_model, h * d_v)
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self.fc_o = nn.Linear(h * d_v, d_model)
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self.dropout = nn.Dropout(dropout)
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self.gamma = nn.Parameter(torch.randn((1, h, 1, 1)))
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self.d_model = d_model
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self.d_k = d_k
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self.d_v = d_v
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self.h = h
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self.init_weights()
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, mode='fan_out')
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if m.bias is not None:
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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init.normal_(m.weight, std=0.001)
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if m.bias is not None:
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init.constant_(m.bias, 0)
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def forward(self, queries, keys, values):
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b_s, nq = queries.shape[:2]
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nk = keys.shape[1]
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q = self.fc_q(queries).view(b_s, nq, self.h, self.d_k).permute(0, 2, 1, 3) # (b_s, h, nq, d_k)
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k = self.fc_k(keys).view(b_s, nk, self.h, self.d_k).permute(0, 2, 3, 1) # (b_s, h, d_k, nk)
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v = self.fc_v(values).view(b_s, nk, self.h, self.d_v).permute(0, 2, 1, 3) # (b_s, h, nk, d_v)
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kv = torch.matmul(k, v) # bs,h,c,c
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kv_norm = XNorm(kv, self.gamma) # bs,h,c,c
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q_norm = XNorm(q, self.gamma) # bs,h,n,c
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out = torch.matmul(q_norm, kv_norm).permute(0, 2, 1, 3).contiguous().view(b_s, nq, self.h * self.d_v)
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out = self.fc_o(out) # (b_s, nq, d_model)
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return out
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if __name__ == '__main__':
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block = UFOAttention(d_model=512, d_k=512, d_v=512, h=8).cuda()
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input = torch.rand(64, 64, 512).cuda()
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output = block(input, input, input)
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print(input.size(), output.size())
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